AI Boilerplates

Explore 58 boilerplates in this collection. Find the perfect starting point for your next project.

Visit website for PySaaS

PySaaS

Build a profitable SaaS business faster in pure Python

Python
Firestore
SQLite
Supabase
Lemon Squeezy
Next.js
Reflex

Features:

AI
Analytics
API
Auth
Blog
Deployment
Landing Page
+3 more
Visit website for Gravity

Gravity

The original Node.js & React SaaS boilerplate with subscription billing, authentication, and UI components.

JavaScript
React
shadcn/ui
Amazon Redshift
MariaDB
MongoDB
MSSQL
MySQL
Oracle
PostgreSQL
SQLite
Stripe
Next.js
Node.js
React
React Native

Features:

2FA
Access Control
Admin
AI
API
Auth
Dark Mode
+11 more
Visit website for LaunchFast

LaunchFast

Production-Ready SaaS Starter Kits in Astro, Next.js, and SvelteKit

JavaScript
TypeScript
HTML
React
Tailwind CSS
DynamoDB
Firestore
MongoDB
PostgreSQL
Redis
SQLite
Lemon Squeezy
Stripe
Astro
Next.js
Preact
React
SolidJS
Svelte
SvelteKit
Vue.js

Features:

AI
Analytics
Auth
Blog
ContentLayer
Docs
Emails
+4 more
Visit website for Super SaaS

Super SaaS

The Simple, Fast & Smart Nuxt 3 Fullstack Kit

JavaScript
TypeScript
Nuxt UI
Radix Vue
shadcn/vue
Tailwind CSS
Drizzle ORM
Lemon Squeezy
Stripe
Nuxt

Features:

Admin
AI
API
Auth
Dark Mode
Emails
ORM
+6 more
Visit website for AnotherWrapper

AnotherWrapper

10 customizable AI demo apps to build your AI startup in hours

JavaScript
TypeScript
DaisyUI
shadcn/ui
Tailwind CSS
PostgreSQL
Supabase
Lemon Squeezy
Stripe
Next.js
React

Features:

AI
Analytics
Auth
Blog
ChatGPT
Emails
OpenAI
+1 more
Visit website for Makerkit

Makerkit

A SaaS Starter Kit for building production-ready React applications

JavaScript
TypeScript
Lucide Icons
Radix UI
shadcn/ui
Tailwind CSS
Firestore
Supabase
Lemon Squeezy
Stripe
Next.js
React
React Native
Remix

Features:

2FA
Admin
AI
Analytics
Auth
Blog
Dark Mode
+16 more
Visit website for Codepilot

Codepilot

The Ultimate SaaS Starter Kit with all you need to ship fast.

JavaScript
TypeScript
React
Tailwind CSS
PostgreSQL
Supabase
Lemon Squeezy
Stripe
Next.js
Strapi

Features:

Admin
AI
Animations
Auth
Blog
Dashboard
Emails
+7 more
Visit website for Nuxflare Pro

Nuxflare Pro

The Complete Nuxt + Cloudflare Starter Kit

JavaScript
TypeScript
Nuxt
Drizzle ORM
Paddle
Stripe
Nuxt
Pulumi
SST.dev
tRPC

Features:

Access Control
AI
Analytics
Auth
Background Jobs
Billing
Caching
+11 more
Visit website for ShipThatApp

ShipThatApp

Accelerate your SwiftUI app development with integrated AI and secure backend solutions

Swift
SwiftUI
Supabase
RevenueCat
StoreKit 2
SwiftUI

Features:

AI
Analytics
API
Auth
ChatGPT
Dark Mode
Deployment
+7 more

Showing 9 of 58 boilerplates

Why Choose AI Boilerplates?

AI represents a complete full-stack feature with dedicated API endpoints, database models, and UI components architected for SaaS applications. Our boilerplates with AI implement layered architecture patterns—separating business logic, data access, and presentation—with security measures and testing strategies specific to AI's functionality.

AI boilerplates implement full-stack architecture with service layers for business logic, repository patterns for data access, and RESTful/GraphQL API endpoints. They include AI-specific security measures like input validation with schema libraries (Zod, Joi), parameterized queries for SQL injection prevention, and CSRF protection. The implementation handles AI's real-time requirements with WebSockets or SSE when needed, includes comprehensive error handling, and follows OWASP security guidelines for AI's functionality.

Key Benefits

  • AI layered architecture
  • AI-specific security measures
  • AI API endpoint design
  • AI real-time capabilities
  • AI validation schemas
  • AI error handling
  • AI testing suite
  • AI performance optimization

Browse our collection of 58 AI boilerplates to find the perfect starting point for your next SaaS project. Each boilerplate has been carefully reviewed to ensure quality, security, and production-readiness.

Frequently Asked Questions

How is AI architecturally implemented?

AI is implemented following full-stack architecture patterns with dedicated API endpoints, database models with proper relationships, and corresponding UI components. The feature includes its own service layer for business logic, validation schemas, error handling, and event-driven updates. The architecture separates concerns between presentation, business logic, and data access layers, making AI maintainable and testable.

What security measures protect AI?

AI implements defense-in-depth security including input validation with schema validation libraries (Zod, Joi, Yup), parameterized database queries to prevent SQL injection, output encoding to prevent XSS attacks, CSRF token validation, and proper authentication/authorization checks. The feature includes rate limiting, audit logging, and follows OWASP security guidelines specific to AI's functionality.

How does AI handle real-time updates?

AI can include real-time capabilities using WebSockets, Server-Sent Events (SSE), or polling strategies depending on the use case. Real-time implementations use Socket.io, native WebSockets, or framework-specific solutions with proper connection management, authentication, and scaling considerations. The feature handles reconnection logic, message queuing, and optimistic UI updates for responsive user experience.

What API patterns does AI use?

AI's API endpoints follow RESTful principles or GraphQL patterns with proper HTTP methods, status codes, and response structures. The implementation includes request validation, pagination for list endpoints, filtering and sorting capabilities, and comprehensive error responses with meaningful messages. API versioning, rate limiting per endpoint, and OpenAPI/GraphQL schema documentation are included for AI's public-facing endpoints.

How is AI tested and validated?

AI includes unit tests for business logic, integration tests for API endpoints and database interactions, and end-to-end tests for critical user flows. The testing suite uses framework-specific tools (Jest, Pytest, RSpec, PHPUnit) with mocking libraries, test fixtures, and database seeding. Tests cover happy paths, error cases, edge conditions, and security scenarios specific to AI's functionality with proper test coverage reporting.